Just what is “Actionable Insight” for?

We all use terms 'big data' and 'actionable insight' but what do they mean?

The ‘Big Data’ catchphrase is being bandied around like so many slogans before it to justify spending ever more on data reporting.

With more data than ever, and with new data increasing at an increasing rate, it is a very valid desire to want to have great data handling and engaging visualisation, easily available. I couldn’t agree more.

However, of course, the data is not important for its own sake. Very few organisations exist with an outcome that is to ‘present the data’.

In fact, for organisations other than data science companies the data is just one cog in one step in the cycle that is managing business improvement.

To this end the Big Data catchphrase has been neatly tied to the cycle by bringing in the slogan of ‘Actionable Insight’.

“Big Data visualisation provides Actionable Insight”.

That is a great step forward from just ‘fantastic data visualisation’. There is a recognition that the visualization is not an end itself, but leads to the user having ‘insight’.

But that is where they run out of steam.

Having provided Actionable Insight (if and when they do) these data visualisation tools stop and leave you to get on with the rest of whatever Actionable Insight is for, for yourself.

The purpose of Actionable Insight is to agree the action, plan the action, take the action and monitor its impact, in order to improve outcome performance. Every action we plan is in effect a hypothesis that we are testing. “If we take the action of employing more contact centre staff it will reduce the average time it takes for us to answer the phone, so we will get fewer lost calls and as a consequence we will get a greater number of successful bookings”.

We may need to discuss the insight with others and to agree that employing more contact centre staff is both appropriate and affordable as an action. Once the action is agreed we allocate responsibility for achieving that action to someone. We may want to track the progress on the action, so that we can focus on it more if it is not progressing according to plan, and that it gets completed.

Now we need to monitor the effect.

The Example...

Does the average time to answer calls fall, does the number of missed calls reduce, and does that lead to a greater number of successful bookings?

This is what a joined up solution would do for you. You would be able to see your Actionable Insight, and discuss potential actions alongside the data visualisation that created the actionable insight. Once the action is agreed you could create an associated action, allocate responsibility and track its progress.

Once the action is completed you could then see whether the measure to which it is associated improves, in this case the average response time to answer calls, and the impact that has on other measures (through the cause and effect links) in this case on lost calls and subsequently on the number of successful bookings. If the action of employing more contact centre staff had the desired effect – bravo – a completed plan-do-review cycle that delivered improvement! Your outcome performance has improved in the key measure and the organisations performance has improved.

If it did not have the desired effect on improving the outcome results – don’t despair. Let’s look at possible causes of that lack of expected impact, and gain better insight. What did our hypothesis miss? Did the new recruits answer the calls and produce less missed calls? Yes? But then did the ratio of calls to bookings reduce? Yes, is that only for the new starts? Yes? Let’s discuss whether the new starts have the same levels of skills as our existing operators, did we do sufficient training? Did we train the right things? Did we recruit the wrong people? Have we just got the maximum amount of bookings we can get - due to capacity constraints, or competition, or the total demand?

"So what is it I need?"

Actionable Insight is only useful if you can then act, monitor and check on the completion of the actions, and track their successful impact. Often we don’t get it right first time and often we then need to iterate with related discussion and insights and refine the actions in a continuous improvement cycle to ensure outcomes improve.

What you want is the management action cycle supported in your data visualisation solution.

Is that available? Yes. But it is not available in any of the big name Big Data Visualisation tools. It is only available in an enterprise performance management tool that also has great data visualisation capabilities combined with an inherent built in functionality for the plan-do-check-act cycle. Fortunately for me, it’s the one we produce.